research-article

Trapezius muscle EMG as predictor of mental stress

Authors:

Jacqueline Wijsman,

Bernard Grundlehner,

Julien Penders,

Hermie HermensAuthors Info & Claims

WH '10: Wireless Health 2010

Pages 155 - 163

https://doi.org/10.1145/1921081.1921100

Published: 05 October 2010 Publication History

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Abstract

Stress is a growing problem in society and can, amongst others, induce musculoskeletal complaints, related to sustained muscle tension. The ability to measure stress with a wireless system would be useful in the prevention of stress-related health problems. The aim of this experiment was to derive stress levels of subjects from electromyography (EMG) signals of the upper trapezius muscle. Two new stress tests were designed for this study, which aimed at creating circumstances that are similar to work stress.

An experiment is described in which EMG signals of the upper trapezius muscle were measured during three different stressful situations. Stress tests included a calculation task (the Norinder test), a logical puzzle task and a memory task, of which the last two were newly designed.

The results show significantly higher amplitudes of the EMG signals during stress compared to rest and fewer gaps (periods of relaxation) during stress. Also, mean and median frequencies were significantly lower during stress than during rest. The differences in EMG features between rest and stress conditions indicate that EMG is a useful parameter to detect stress. These results show opportunities for the inclusion of EMG sensors in a wireless system for ambulatory monitoring of stress levels.

References

[1]

K. Allen and J. Blascovich. Effects of music on cardiovascular reactivity among surgeons. Journal Of the American Medical Association, 272:882--884, 1994.

Crossref

Google Scholar

[2]

L. Brown, B. Grundlehner, J. van de Molengraft, J. Penders, and B. Gyselinckx. Body area network for monitoring autonomic nervous system responses. In Pervasive Computing Technologies for Healthcare, 2009.

Google Scholar

[3]

S. Cohen, T. Kamarck, and R. Mermelstein. A global measure of perceived stress. Journal of Health and Social Behavior, 24:385--396, 1983.

Crossref

Google Scholar

[4]

N. Hjortskov, D. Rissén, A. Blangsted, N. Fallentin, U. Lundberg, and K. Søgaard. The effect of mental stress on heart rate variability and blood pressure during computer work. European Journal of Applied Physiology, 92(1):84--89, June 2004.

Crossref

Google Scholar

[5]

G. Krantz, M. Forsman, and U. Lundberg. Consistency in physiological stress responses and electromyographic activity during induced stress exposure in women and men. Integrative Psychological and Behavioral Science, 39(2):105--118, 2004.

Crossref

Google Scholar

[6]

S.-E. Larsson, R. Larsson, Q. Zhang, H. Cai, and P. A. Öberg. Effects of psychophysiological stress on trapezius muscles blood flow and electromyography during static load. European Journal of Applied Physiology and Occupational Physiology, 71(6):493--498, 1995.

Crossref

Google Scholar

[7]

B. Laursen, B. Jensen, A. Garde, and A. Jørgensen. Effect of mental and physical demands on muscular activity during the use of a computer mouse and a keyboard. Scand. J. Work. Environ. Health, 28:215--221, 2002.

Crossref

Google Scholar

[8]

M. Loeb, D. H. Holding, and M. A. Baker. Noise stress and circadian arousal in self-paced computation. Motivation and Emotion, 6:43--48, 1982.

Crossref

Google Scholar

[9]

U. Lundberg, R. Kadefors, B. Melin, G. Palmerud, P. Hassmen, M. Engstrom, and I. E. Dohns. Psychophysiological stress and EMG activity of the trapezius muscle. International Journal of Behavioral Medicine, 1:354--370, 1994.

Crossref

Google Scholar

[10]

F. Martini. Fundamentals of Anatomy and Physiology. Prentice Hall, 4 edition, 1998.

Google Scholar

[11]

R. Merletti and P. Parker. Electromyography - Physiology, Engineering and Noninvasive Applications. John Wiley & Sons, Inc., Hoboken, New Jersey, 2004.

Google Scholar

[12]

M. L. Peters, G. L. R. Godaert, R. E. Ballieux, M. van Vliet, J. J. Willemsen, F. C. G. J. Sweep, and C. J. Heijnen. Cardiovascular and endocrine responses to experimental stress: Effects of mental effort and controllability. Psychoneuroendocrinology, 23(1):1--17, Jan. 1998.

Crossref

Google Scholar

[13]

D. Rissén, B. Melin, L. Sandsjö, I. Dohns, and U. Lundberg. Surface EMG and psychophysiological stress reactions in women during repetitive work. European Journal of Applied Physiology, 83:215--222, 2000.

Crossref

Google Scholar

[14]

I. Romero, M. Xie, A. Link, R. D. Bousseljot, D. Kreiseler, C. Elster, M. Bär, and H. Koch. Analysis of the atrial fibrillation main frequency in the PTB ECG database. In European Study Group on Cardiovascular Oscillations, pages 120--122, 2006.

Google Scholar

[15]

L. M. Schleifer, T. W. Spalding, S. E. Kerick, J. R. Cram, R. Ley, and B. D. Hatfield. Mental stress and trapezius muscle activation under psychomotor challenge: a focus on EMG gaps during computer work. Psychophysiology, 45:356--365, 2008.

Crossref

Google Scholar

[16]

SENIAM. Surface electromyography for the non-invasive assessment of muscles, April 2009.

Google Scholar

[17]

T. W. Spalding, L. M. Schleifer, B. D. Hatfield, S. Kerick, and J. R. Cram. Removing the influence of the heart from surface-recorded EMG. Biofeedback, 31:6--10, 2003.

Google Scholar

[18]

C. Tsigos and G. P. Chrousos. Hypothalamic-pituitary-adrenal axis, neuroendocrine factors and stress. Journal of Psychosomatic Research, 53:865--871, 2002.

Crossref

Google Scholar

[19]

R. F. Yazicioglu, P. Merken, R. Puers, and C. Van Hoof. A 60 μW 60 nV/√Hz readout front-end for portable biopotential acquisition systems. IEEE Journal of Solid-State Circuits, 42:1100--1110, 2007.

Crossref

Google Scholar

[20]

J. Zhai and A. Barreto. Stress detection in computer users based on digital signal processing of noninvasive physiological variables. In Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE DOI - 10.1109/IEMBS.2006.259421, pages 1355--1358, 2006.

Crossref

Google Scholar

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Taskasaplidis GFotiadis DBamidis P(2024)Review of Stress Detection Methods Using Wearable SensorsIEEE Access10.1109/ACCESS.2024.337301012(38219-38246)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3373010
Hossain NNoushin TTabassum S(2024)StressFit: a hybrid wearable physicochemical sensor suite for simultaneously measuring electromyogram and sweat cortisolScientific Reports10.1038/s41598-024-81042-514:1Online publication date: 29-Nov-2024
https://doi.org/10.1038/s41598-024-81042-5
Zechner OSchrom-Feiertag HUhl JNguyen QKleygrewe LTscheligi M(2023)Mind the Heart: Designing a Stress Dashboard Based on Physiological Data for Training Highly Stressful Situations in Virtual RealityHuman-Computer Interaction – INTERACT 202310.1007/978-3-031-42293-5_16(209-230)Online publication date: 26-Aug-2023
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Index Terms

Trapezius muscle EMG as predictor of mental stress
1. Applied computing
  1. Law, social and behavioral sciences
    1. Psychology
  2. Life and medical sciences
    1. Consumer health
    2. Health informatics

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Reviews

Reviewer: Goran Trajkovski

In today's world, which is filled with stressful situations, researchers are diligently studying the manifestations of stress. They hope to, among other things, come up with practical solutions for monitoring stress levels. As stress (whatever the definition) affects muscles, electromyography (EMG) frequencies are observed in the context of stressful situations and juxtaposed with an individual's self-report. This juxtaposition helps researchers determine if tests designed to, say, mimic the stress in the workplace define a notable relation between the EMG signals and the subjects' reports. This remarkable paper by Wijsman et al. is full of graphical elements that support their study of EMG signals of the back/neck trapezoid muscle as a potential predictor for mental stress. Researchers examined data from 30 subjects who were put through three stressful situations (calculations, logic puzzles, and memory tests). Of those, 22 individual datasets were used for a remarkable statistical analysis. The goal of the study was to propose and validate a protocol of stress induction, confirm that stress can be detected from the EMG, and investigate the manifestation of stress in the EMG spectrum. What we learn from this paper is that the designed protocol meets these goals and indicates an EMG shift toward lower frequencies during stress, a situation not unlike what happens to muscles under fatigue. So, what is next__?__ Apart from looking into the origins of differences detected in stressful scenarios, perhaps a wireless commercial product can be built to monitor stress in an ambulatory setting. Online Computing Reviews Service

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Published In

WH '10: Wireless Health 2010

October 2010

232 pages

ISBN:9781605589893

DOI:10.1145/1921081

General Chairs:
Irwin Mark Jacobs
Qualcomm Incorporated
,
Patrick Soon-Shiong
UCLA Wireless Health Institute
,
Eric Topol
West Wireless Institute
,
Chris Toumazou
Institute of Biomedical Engineering, Imperial College London

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SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 October 2010

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WH '10

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WLSA

WH '10: Wireless Health 2010

October 5 - 7, 2010

California, San Diego

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Overall Acceptance Rate 35 of 139 submissions, 25%

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https://doi.org/10.1109/ACCESS.2024.3373010
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https://doi.org/10.1038/s41598-024-81042-5
Zechner OSchrom-Feiertag HUhl JNguyen QKleygrewe LTscheligi M(2023)Mind the Heart: Designing a Stress Dashboard Based on Physiological Data for Training Highly Stressful Situations in Virtual RealityHuman-Computer Interaction – INTERACT 202310.1007/978-3-031-42293-5_16(209-230)Online publication date: 26-Aug-2023
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